Evaluation of oxygen exposure levels and polyphenolic content of red wines using an electronic panel formed by an electronic nose and an electronic tongue

Traditional method of rhubarb processing optimized by combining flavor analysis with anthraquinone content determination

Introduction

Rhubarb is a popular food that relieves constipation and aids with weight loss. The traditional method of preparation, includes steaming and sun-drying rhubarb nine times (SDR-9) to reduce its toxicity and increase efficacy.

Methods

Flavor analysis includes odor analysis by gas chromatography-ion mobility spectrometry and taste characterization using an electronic tongue.

Results

Odor analysis of the samples prepared through SDR-9 identified 61 volatile compounds, including aldehydes, esters, alcohols, ketones, acids, alkenes, and furans. Of these, 13 volatile components were the key substances associated with odor. This enabled the process to be divided into two stages: 1-5 times of steaming and sun-drying and 6-9 times. In the second stage, SDR-6 and SDR-9 were grouped together in terms of odor. Analysis using electronic tongue revealed that the most prominent taste was bitterness. A radar map indicated that the bitterness response was the highest for raw rhubarb, whereas that for processed (steamed and sun-dried) rhubarb decreased. Orthogonal partial least squares discriminant analysis (OPLS-DA) clustering results for SDR-6 and SDR-9 samples indicated that their tastes were similar. Anthraquinones were analyzed via high-performance liquid chromatography; moreover, analysis of the taste and components of the SDR samples revealed a significant correlation.

Discussion

These results indicate that there are similarities between SDR-6 and SDR-9 in terms of smell, taste, and composition, indicating that the steaming and sun-drying cycles can be conducted six times instead of nine.

Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R² and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

Classification and Antioxidant Activity Evaluation of Edible Oils by Using Nanomaterial-Based Electrochemical Sensors

The classification of olive oils and the authentication of their biological or geographic origin are important issues for public health and for the olive oil market and related industries. The development of techniques for olive oil classification that are fast, easy to use, and suitable for online, in situ and remote operation is of high interest. In this study, the possibility of discriminating and classifying vegetable oils according to different criteria related to biological or geographical origin was assessed using cyclic voltammograms (CVs) as input data, obtained with electrochemical sensors based on carbonaceous nanomaterials and gold nanoparticles. In this context, 44 vegetable oil samples of different categories were analyzed and the capacity of the sensor array coupled with multivariate analysis was evaluated. The characteristics highlighted in voltammograms are related to the redox properties of the electroactive compounds, mainly phenolics, existing in the oils. Moreover, the antioxidant activity of the oils' hydrophilic fraction was also estimated by conventional spectrophotometric methods (1,1-diphenyl-2-picrylhydrazyl (DPPH) and galvinoxyl) and correlated with the voltammetric responses of the sensors. The percentage of DPPH and galvinoxyl inhibition was accurately predicted from the voltammetric data, with a correlation coefficients greater than 0.97 both in calibration and in validation. The results indicate that this method allows for a clear discrimination of oils from different biological or geographic origins.

Development of a Chemically Modified Sensor Based on a Pentapeptide and Its Application for Sensitive Detection of Verbascoside in Extra Virgin Olive Oil

In addition to their antioxidant and antimicrobial action in functional foods, beverages, and in some dermato-cosmetic products, olive phenolic compounds are also recognized for their role in the prevention of diabetes and inflammation, treatment of heart disease and, consequently, of the numerous chronic diseases mediated by the free radicals. In recent years, attention has increased, in particular, regarding one of the most important compound in extra virgin olive oil (EVOO) having glycosidic structure, namely verbocoside, due to the existence in the literature of numerous studies demonstrating its remarkable contribution to the prophylaxis and treatment of various disorders of the human body. The purpose of this study was the qualitative and quantitative determination of verbascoside in commercial EVOOs from different regions by means of a newly developed sensor based on a screen-printed carbon electrode (SPCE) modified with graphene oxide (GPHOX), on the surface of which a pentapeptide was immobilized by means of glutaraldehyde as cross-linking agent. The modified electrode surface was investigated using both Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) methods. This newly developed sensor has shown a high sensibility compared to the unmodified electrode, a low detection limit (LOD) of up to 9.38 × 10^-8 M, and a wide linearity range between 0.1 µM and 10.55 µM. The applicability of the modified sensor was confirmed by detecting verbascoside in ten different EVOOs samples using the cyclic voltammetry (CV) method, with very good results. The validation of the electroanalytical method was performed by using the standard addition method with very good recoveries in the range of 97.48-103.77%.

Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm^-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r_p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.

Bacterial Diversity, Organic Acid, and Flavor Analysis of Dacha and Ercha Fermented Grains of Fen Flavor Baijiu

Fen flavor Baijiu needs two rounds of fermentation, which will obtain Dacha after initial fermentation and Ercha after secondary fermentation. The quality of Baijiu is closely related to the microbes within fermented grains. However, the bacterial diversity in Dacha and Ercha fermented grains of Fen flavor Baijiu has not been reported. In the present study, the structure and diversity of bacteria communities within fermented grains of Fen flavor Baijiu were analyzed and evaluated using MiSeq platform's HTS with a sequencing target of the V3-V4 region of the 16S rRNA gene. Through the analysis of physical and chemical indexes and electronic senses, the relationship between bacterial flora, organic acid, taste, and aroma in fermented grains was clarified. The results indicated that Lactobacillus was the main bacteria in Dacha, and the mean relative content was 97.53%. The bacteria within Ercha samples were Pseudomonas and Bacillus, mean relative content was 37.16 and 28.02%, respectively. The diversity of bacterial communities in Ercha samples was significantly greater than that in Dacha samples. The correlation between Lactobacillus and organic acids, especially lactic acid, led to the difference between Dacha and Ercha organic acids, which also made the pH value of Dacha lower and the sour taste significantly higher than Ercha. Lactobacillus was significantly positively correlated with a variety of aromas, which made Dacha the response value of aromas higher. In addition, Bacillus had a significant positive correlation with bitterness and aromatic compounds, which led to a higher response value of bitterness in Ercha and made it present an aromatic aroma. This study provides an in-depth analysis of the difference between different stages of Fen flavor Baijiu, and theoretical support for the standard production and improvement in quality of Fen flavor Baijiu in the future.

Evaluation of flavor characteristics of bacon smoked with different woodchips by HS-SPME-GC-MS combined with an electronic tongue and electronic nose

This study investigated the effects of different woodchip types (beech, oak, pear, and apple) on the volatile compounds and sensory characteristics of smoked bacon. The volatile compounds were influenced by woodchip types and the total content of ketones and phenols in pear-smoked bacon were higher than in bacon smoked with other woodchips (P < 0.05). The E-tongue combined with E-nose can effectively distinguish the difference in the flavor of bacon smoked with different woodchip types by the signal intensities. Sensory analysis showed that smoking increased bacon's redness, saltiness, and smoky flavor compared with the control (unsmoked bacon) (P < 0.05) and it had little impact on off-odor (P > 0.05). Correlation analysis showed that the E-nose and E-tongue data were highly correlated with contents of alcohols, aldehydes, and ketones. This study revealed that the different smoked materials greatly influenced the flavor and sensory properties of bacon.

Rice varieties affect bacterial diversity, flavor, and metabolites of zha-chili

The structure and diversity of bacterial communities in spontaneously fermented zha-chili prepared using two different rice varieties (glutinous rice and indica rice) were investigated using high-throughput sequencing. Through metabolic pathway prediction, electronic senses and metabolite analysis, the relationships among the rice varieties used for preparation and the bacterial microbiota, flavor, and organic acid/amino acid metabolites in zha-chili were elucidated. We observed that the structure of bacterial communities in zha-chili samples differed significantly with the rice variety used during fermentation (p < 0.05), and that there was a greater abundance of bacterial species in zha-chili prepared using glutinous rice. Lactic acid bacteria were predominant in zha-chili, with an average relative abundance of 77.09%. The aroma of zha-chili was influenced by the raw material itself, while the characteristic tastes of zha-chili - including sourness, umami and richness - were significantly correlated with the bacterial microbiota. In addition, the abundance of lactic acid bacteria was positively correlated with the levels of organic acids and negatively correlated with the levels of amino acids. This also made the zha-chili prepared using glutinous rice sourer and imparted more umami taste to the zha-chili prepared using indica rice. Our observations provide a reference for the evaluation of zha-chili quality and could effectively guide the improvement of zha-chili products.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Odor Detection Using an E-Nose With a Reduced Sensor Array

Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola’s Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.

Analysis of musts and wines by means of a bio-electronic tongue based on tyrosinase and glucose oxidase using polypyrrole/gold nanoparticles as the electron mediator

A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process.

Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines

A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.

Untargeted voltammetric approaches for characterization of oxidation patterns in white wines

Chemical and electrochemical changes associated with controlled oxidation were measured in thirteen commercial white wines, in order to evaluate the potential of linear sweep voltammetry to provide relevant information on the oxidative behavior of individual wines. For a given amount of oxygen consumed, substantial diversity of oxidative behaviors was observed. A good correlation (R² = 0.69) was observed between the rate of O₂ consumption of individual wines and the total charged passed during linear sweep voltammetry, but not with their Folin-Ciocalteu values. Onset potential of anodic oxidation was also related to oxygen consumption capacity of wine, indicating an important contribution of easily oxidizable substrates. Subtraction of voltammograms of oxidized wines from their corresponding non-oxidized controls generated new voltammograms representative of the global changes induced by oxidation. These new voltammograms contained several features related to oxygen consumption rates of each wine, and could be considered as a 'wine oxidation signature'.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

A different approach for the analysis of grapes: Using the skin as sensing element

In this work, an alternative method to monitor the phenolic maturity of grapes was developed. In this approach, the skins of grapes were used to cover the surface of carbon paste electrodes and the voltammetric signals obtained with the skin-modified sensors were used to obtain information about the phenolic content of the skins. These sensors could easily detect differences in the phenolic composition of different Spanish varieties of grapes (Mencía, Prieto Picudo and Juan García). Moreover, sensors were able to monitor changes in the phenolic content throughout the ripening process from véraison until harvest. Using PLS-1 (Partial Least Squares), correlations were established between the voltammetric signals registered with the skin-modified sensors and the phenolic content measured by classical methods (Glories or Total Polyphenol Index). PLS-1 models provided additional information about Brix degree, density or sugar content, which usually used to establish the harvesting date. The quality of the correlations was influenced by the maturation process and the structural and mechanical skin properties. Thus the skin sensors fabricated with Juan García and Prieto Picudo grapes (that showed faster polyphenolic maturation and a higher amount of extractable polyphenols than Mencía), showed good correlations and therefore could be used to monitor the ripening.

A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste.

Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics

Electronic nose and tongue sensors and chemometric multivariate analysis were applied to characterize and classify 7 Chinese robusta coffee cultivars with different roasting degrees. Analytical data were obtained from 126 samples of roasted coffee beans distributed in the Hainan Province of China. Physicochemical qualities, such as the pH, titratable acidity (TA), total soluble solids (TSS), total solids (TS), and TSS/TA ratio, were determined by wet chemistry methods. Data fusion strategies were investigated to improve the performance of models relative to the performance of a single technique. Clear classification of all the studied coffee samples was achieved by principal component analysis, K-nearest neighbour analysis, partial least squares discriminant analysis, and a back-propagation artificial neural network. Quantitative models were established between the sensor responses and the reference physicochemical qualities, using partial least squares regression (PLSR). The PLSR model with a fusion data set was considered the best model for determining the quality parameters.

Electronic Noses and Tongues in Wine Industry

The quality of wines is usually evaluated by a sensory panel formed of trained experts or traditional chemical analysis. Over the last few decades, electronic noses (e-noses) and electronic tongues have been developed to determine the quality of foods and beverages. They consist of arrays of sensors with cross-sensitivity, combined with pattern recognition software, which provide a fingerprint of the samples that can be used to discriminate or classify the samples. This holistic approach is inspired by the method used in mammals to recognize food through their senses. They have been widely applied to the analysis of wines, including quality control, aging control, or the detection of fraudulence, among others. In this paper, the current status of research and development in the field of e-noses and tongues applied to the analysis of wines is reviewed. Their potential applications in the wine industry are described. The review ends with a final comment about expected future developments.

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

The capability of a phthalocyanine-based voltammetric electronic tongue to analyze strong alcoholic beverages has been evaluated and compared with the performance of spectroscopic techniques coupled to chemometrics. Nalewka Polish liqueurs prepared from five apple varieties have been used as a model of strong liqueurs. Principal Component Analysis has demonstrated that the best discrimination between liqueurs prepared from different apple varieties is achieved using the e-tongue and UV-Vis spectroscopy. Raman spectra coupled to chemometrics have not been efficient in discriminating liqueurs. The calculated Euclidean distances and the k-Nearest Neighbors algorithm (kNN) confirmed these results. The main advantage of the e-tongue is that, using PLS-1, good correlations have been found simultaneously with the phenolic content measured by the Folin-Ciocalteu method (R² of 0.97 in calibration and R² of 0.93 in validation) and also with the density, a marker of the alcoholic content method (R² of 0.93 in calibration and R² of 0.88 in validation). UV-Vis coupled with chemometrics has shown good correlations only with the phenolic content (R² of 0.99 in calibration and R² of 0.99 in validation) but correlations with the alcoholic content were low. Raman coupled with chemometrics has shown good correlations only with density (R² of 0.96 in calibration and R² of 0.85 in validation). In summary, from the three holistic methods evaluated to analyze strong alcoholic liqueurs, the voltammetric electronic tongue using phthalocyanines as sensing elements is superior to Raman or UV-Vis techniques because it shows an excellent discrimination capability and remarkable correlations with both antioxidant capacity and alcoholic content-the most important parameters to be measured in this type of liqueurs.

The fungicide triadimefon affects beer flavor and composition by influencing Saccharomyces cerevisiae metabolism

Despite the fact that beer is produced on a large scale, the effects of pesticide residues on beer have been rarely investigated. In this study, we used micro-brewing settings to determine the effect of triadimefon on the growth of Saccharomyces cerevisiae and beer flavor. The yeast growth in medium was significantly inhibited (45%) at concentrations higher than 5 mg L(-1), reaching 80% and 100% inhibition at 10 mg L(-1) and 50 mg L(-1), respectively. There were significant differences in sensory quality between beer samples fermented with and without triadimefon based on data obtained with an electronic tongue and nose. Such an effect was most likely underlain by changes in yeast fermentation activity, including decreased utilization of maltotriose and most amino acids, reduced production of isobutyl and isoamyl alcohols, and increased ethyl acetate content in the fungicide treated samples. Furthermore, yeast metabolic profiling by phenotype microarray and UPLC/TOF-MS showed that triadimefon caused significant changes in the metabolism of glutathione, phenylalanine and sphingolipids, and in sterol biosynthesis. Thus, triadimefon negatively affects beer sensory qualities by influencing the metabolic activity of S. cerevisiae during fermentation, emphasizing the necessity of stricter control over fungicide residues in brewing by the food industry.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately.

Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.

PRACTICAL APPLICATION

Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile.

Molecularly imprinted polymers as recognition materials for electronic tongues

For over three decades now, molecularly imprinted polymers (MIPs) have successfully been used for selective chemical sensing because the shape and size of their imprinted molecular cavities perfectly matched those of the target analyte molecules. Moreover, orientation of recognizing sites of these cavities corresponded to those of the binding sites of the template molecules. In contrast, electronic tongue (e-tongue) is usually an array of low-affinity recognition units. Its selectivity is based on recognition pattern or multivariate analysis. Merging these two sensing devices led to a synergetic hybrid sensor, an MIP based e-tongue. Fabrication of these e-tongues permitted simultaneous sensing and discriminating several analytes in complex solutions of many components so that these arrays compensated for limitation in cross-reactivity of MIPs. Apparently, analytical signals generated by MIP-based e-tongues, compared to those of ordinary sensor arrays, were more reliable where a unique pattern or 'fingerprint' for each analyte was generated. Additionally, several transduction platforms (from spectroscopic to electrochemical) engaged in constructing MIP-based e-tongues, found their broad and flexible applications. The present review critically evaluates achievements in recent developments of the MIP based e-tongues for chemosensing.

Data fusion methodologies for food and beverage authentication and quality assessment - a review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.

Coupling gas chromatography and electronic nose detection for detailed cigarette smoke aroma characterization

Aroma characterization of whole cigarette smoke samples using sensory panels or electronic nose (E-nose) devices is difficult due to the masking effect of major constituents and solvent used for the extraction step. On the other hand, GC in combination with olfactometry detection does not allow to study the delicate balance and synergetic effect of aroma solutes. To overcome these limitations a new instrumental set-up consisting of heart-cutting gas chromatography using a capillary flow technology based Deans switch and low thermal mass GC in combination with an electronic nose device is presented as an alternative to GC-olfactometry. This new hyphenated GC-E-nose configuration is used for the characterization of cigarette smoke aroma. The system allows the transfer, combination or omission of selected GC fractions before injection in the E-nose. Principal component analysis (PCA) and discriminant factor analysis (DFA) allowed clear visualizing of the differences among cigarette brands and classifying them independently of their nicotine content. Omission and perceptual interaction tests could also be carried out using this configuration. The results are promising and suggest that the GC-E-nose hyphenation is a good approach to measure the contribution level of individual compounds to the whole cigarette smoke.

An efficient approach for preprocessing data from a large-scale chemical sensor array

In this paper, an artificial olfactory system (Electronic Nose) that mimics the biological olfactory system is introduced. The device consists of a Large-Scale Chemical Sensor Array (16, 384 sensors, made of 24 different kinds of conducting polymer materials) that supplies data to software modules, which perform advanced data processing. In particular, the paper concentrates on the software components consisting, at first, of a crucial step that normalizes the heterogeneous sensor data and reduces their inherent noise. Cleaned data are then supplied as input to a data reduction procedure that extracts the most informative and discriminant directions in order to get an efficient representation in a lower dimensional space where it is possible to more easily find a robust mapping between the observed outputs and the characteristics of the odors in input to the device. Experimental qualitative proofs of the validity of the procedure are given by analyzing data acquired for two different pure analytes and their binary mixtures. Moreover, a classification task is performed in order to explore the possibility of automatically recognizing pure compounds and to predict binary mixture concentrations.